Uncertainty Modeling of Improved Fuzzy Functions With Evolutionary Systems

This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if ... then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheles...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2008-08, Vol.38 (4), p.1098-1110
Main Authors: Celikyilmaz, A., Turksen, I.B.
Format: Article
Language:English
Subjects:
Algorithms
Biological Evolution
Clustering algorithms
Computer Simulation
Cybernetics
Evolutionary computation
Fuzzy
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Genetics
Intervals
Learning
Manufacturing processes
Mathematical models
Models, Statistical
Shape
Studies
Type-2 fuzzy functions
Uncertain systems
Uncertainty
uncertainty modeling
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Summary:This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if ... then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy function systems induced by learning parameters, as well as fuzzy function structures. The improved fuzzy clustering initially finds hidden structures, and the genetic learning algorithm optimizes interval type-2 fuzzy sets to capture their optimum uncertainty interval. The proposed ET2FF architecture is evaluated using an extensive suite of real-life applications such as manufacturing process and financial market modeling. The results show that the proposed ET2FF method is comparable - if not superior - to earlier FIS in terms of generalization performance and robustness.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2008.924587